Long Term Person Re-Identification from Depth Cameras using Facial and Skeleton Data

Depth cameras enable long term re-identification exploiting 3D information that captures biometric cues such as face and characteristic lengths of the body. In the typical approach, person re-identification is performed using appearance, thus invalidating any application in which a person may change dress across subsequent acquisitions. For example, this is a relevant scenario for home patient monitoring. Unfortunately, face and skeleton quality is not always enough to grant a correct recognition from depth data. Both features are affected by the pose of the subject and the distance from the camera. We propose a model to incorporate a robust skeleton representation with a highly discriminative face feature, weighting samples by their quality. Our method improves rank-1 accuracy especially on short realistic sequences.